Wing Security announced today that it now offers free discovery and a paid tier for automated control over thousands of AI and AI-powered SaaS applications. This will allow companies to better protect their intellectual property (IP) and data against the growing and evolving risks of AI usage.

SaaS applications seem to be multiplying by the day, and so does their integration of AI

**Wing Security announced today that it now offers** **free discovery and a paid tier for automated control** over thousands of AI and AI-powered SaaS applications. This will allow companies to better protect their intellectual property (IP) and data against the growing and evolving risks of AI usage.

SaaS applications seem to be multiplying by the day, and so does their integration of AI capabilities. According to Wing Security, a SaaS security company that researched over 320 companies, a staggering 83.2% use GenAI applications. While this statistic might not come as a surprise, the research showed that 99.7% of organizations use SaaS applications that leverage AI capabilities to deliver their services. This usage of GenAI in SaaS applications that are not 'pure' AI often goes unnoticed by security teams and users alike.

> 70% of the most popular GenAI applications may use your data to train their models, and in many cases it's completely up to you to configure it differently.

**When e**xamining hundreds of AI-using SaaS applications, Wing Security was able to categorize the different ways in which these applications use organizational data, as well as offer a solution to this new threat:

**Data storing:** In some cases, data is stored by the AI for very long periods of time; in others, it can be stored for short periods only. Storing data allows AI learning models, and future models, to continually train on it. That said, the main concern is when considering the many different types of attacks seen on SaaS applications. When an application is compromised, the data it stores might be compromised too.

**Model training:** By processing vast amounts of information, AI systems can identify patterns, trends, and insights that may elude human analysis. Through machine learning algorithms, AI models learn from data and adapt over time, refining their performance and accuracy, resulting in better service to their end users. On the downside, allowing these models to learn your code, patents, sales, and marketing know-how provides AI-using applications with the potential means to commoditize your organization's competitive edge. To some, these knowledge leaks are considered more significant than data leaks

**The human element:** Certain AI applications leverage human validation to ensure the accuracy and reliability of the data they gather. This collaborative approach, often referred to as human-in-the-loop or human-assisted AI, involves integrating human expertise into the algorithmic decision-making process. This results in higher accuracy for the AI model, but also means a human, working for the GenAI application, is exposed to potentially sensitive data and know-how.

****Leveraging automation to combat AI-SaaS risks****

Wing's recently released AI solution guarantees security teams will better adapt to, and control, the ever-growing and practically unstoppable AI usage in their organizations. Their solution follows three basic steps - Know, Assess, Control.

**Know:** As with many security risks, the first step is to discover them all. In the case of AI, it is not enough to simply flag the "usual suspects" or the pure GenAI applications such as ChatGPT or Bard. With thousands of SaaS applications now using AI to improve their service, discovery must include any application leveraging customer data to improve their models. As with their previous solutions, Wing is offering this first and fundamental step as a free, self-service solution for users to self-onboard and start discovering the magnitude of AI-powered applications used by their employees.

**Assess**: Once AI-using SaaS has been uncovered, Wing automatically provides a security score and details the ways in which company data is used by the AI: How long is it stored for? Is there a human factor? And perhaps most importantly, is it configurable? Providing a detailed view of the application's users, permissions, and security information. This automatic analysis allows security teams to make better-informed decisions.

**Control:** Wing's discovery and analysis pin-points the most critical issues to address, allowing security teams to easily understand the level of risk and types of actions needed. For example, deciding whether or not they should permit a certain application's usage or simply configure the AI elements to better match their security policy.

****The Secret: Automating All Of The Above****

By automating Discovery, Assessment and Control, security teams save time on figuring out where to focus their efforts instead of spreading themselves thin trying to solve a huge and evolving attack surface. Subsequently, this significantly reduces risk.

Wing's automated workflows also allow for a unique cross-organizational solution: By allowing users to directly communicate with the application's admin or users, Wing prompts better-informed security solutions alongside a stronger security culture of inclusion rather than simple black or white listing.

In an era where SaaS applications are omnipresent, their integration with artificial intelligence raises a new type of challenge. On the one hand, AI usage has become a great tool for boosting productivity, and employees should be able to use it for its many benefits. On the other hand, as the reliance on AI in SaaS applications continues to surge, the potential risks associated with data usage become more pronounced.

Wing Security has responded to this challenge by introducing a new approach, aimed at empowering organizations to navigate and control the escalating use of AI within their operations, while involving the end users in the loop and ensuring they may use the AI-SaaS they need, safely. Their automated control platform provides a comprehensive understanding of how AI applications utilize organizational data and know-how, addressing issues such as data storing, model training, and the human element in the AI loop. Security teams can save precious time thanks to clear risk-prioritization and user involvement.

Found this article interesting? Follow us on Twitter __ and LinkedIn to read more exclusive content we post.

Combating IP Leaks into AI Applications with Free Discovery and Risk Reduction Automation

Once, drug dealers and money launderers saw cryptocurrency as perfectly untraceable. Then a grad student named Sarah Meiklejohn proved them all wrong—and set the stage for a decade-long crackdown.

How a 27-Year-Old Codebreaker Busted the Myth of Bitcoin’s Anonymity

Cybersecurity researchers have identified a "lightweight method" called iShutdown for reliably identifying signs of spyware on Apple iOS devices, including notorious threats like NSO Group's Pegasus, QuaDream's Reign, and Intellexa's Predator. 
Kaspersky, which analyzed a set of iPhones that were compromised with Pegasus, said the infections left traces in a file

Spyware / Forensic Analysis

Cybersecurity researchers have identified a "lightweight method" called **iShutdown** for reliably identifying signs of spyware on Apple iOS devices, including notorious threats like NSO Group's Pegasus, QuaDream's Reign, and Intellexa's Predator.

Kaspersky, which analyzed a set of iPhones that were compromised with Pegasus, said the infections left traces in a file named "Shutdown.log," a text-based system log file available on all iOS devices and which records every reboot event alongside its environment characteristics.

"Compared to more time-consuming acquisition methods like forensic device imaging or a full iOS backup, retrieving the Shutdown.log file is rather straightforward," security researcher Maher Yamout said. "The log file is stored in a sysdiagnose (sysdiag) archive."

The Russian cybersecurity firm said it identified entries in the log file that recorded instances where "sticky" processes, such as those associated with the spyware, caused a reboot delay, in some cases observing Pegasus-related processes in over four reboot delay notices.

What's more, the investigation revealed a the presence of a similar filesystem path that's used by all the three spyware families – "/private/var/db/" for Pegasus and Reign, and "/private/var/tmp/" for Predator – thereby acting as an indicator of compromise.

That said, the success of this approach hinges on a caveat that the target user reboots their device as often as possible, the frequency for which varies according to their threat profile.

Kaspersky has also published a collection of Python scripts to extract, analyze, and parse the Shutdown.log in order to extract the reboot stats.

"The lightweight nature of this method makes it readily available and accessible," Yamout said. "Moreover, this log file can store entries for several years, making it a valuable forensic artifact for analyzing and identifying anomalous log entries."

The disclosure comes as SentinelOne revealed information stealers targeting macOS such as KeySteal, Atomic, and JaskaGo (aka CherryPie or Gary Stealer) are quickly adapting to circumvent Apple's built-in antivirus technology called XProtect.

"Despite solid efforts by Apple to update its XProtect signature database, these rapidly evolving malware strains continue to evade," security researcher Phil Stokes said. "Relying solely on signature-based detection is insufficient as threat actors have the means and motive to adapt at speed."

Found this article interesting? Follow us on Twitter __ and LinkedIn to read more exclusive content we post.

New iShutdown Method Exposes Hidden Spyware Like Pegasus on Your iPhone

Google on Tuesday released updates to fix four security issues in its Chrome browser, including an actively exploited zero-day flaw.
The issue, tracked as CVE-2024-0519, concerns an out-of-bounds memory access in the V8 JavaScript and WebAssembly engine, which can be weaponized by threat actors to trigger a crash.
"By reading out-of-bounds memory, an attacker might be able to get secret values,

Browser Security / Vulnerability

Google on Tuesday released updates to fix four security issues in its Chrome browser, including an actively exploited zero-day flaw.

The issue, tracked as **CVE-2024-0519**, concerns an out-of-bounds memory access in the V8 JavaScript and WebAssembly engine, which can be weaponized by threat actors to trigger a crash.

"By reading out-of-bounds memory, an attacker might be able to get secret values, such as memory addresses, which can be bypass protection mechanisms such as ASLR in order to improve the reliability and likelihood of exploiting a separate weakness to achieve code execution instead of just denial of service," according to MITRE's Common Weakness Enumeration (CWE).

Additional details about the nature of the attacks and the threat actors that may be exploiting them have withheld in an attempt to prevent further exploitation. The issue was reported anonymously on January 11, 2024.

"Out-of-bounds memory access in V8 in Google Chrome prior to 120.0.6099.224 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page," reads a description of the flaw on the NIST's National Vulnerability Database (NVD).

The development marks the first actively exploited zero-day to be patched by Google in Chrome in 2024. Last year, the tech giant resolved a total of 8 such actively exploited zero-days in the browser.

Users are recommended to upgrade to Chrome version 120.0.6099.224/225 for Windows, 120.0.6099.234 for macOS, and 120.0.6099.224 for Linux to mitigate potential threats.

Users of Chromium-based browsers such as Microsoft Edge, Brave, Opera, and Vivaldi are also advised to apply the fixes as and when they become available.

Found this article interesting? Follow us on Twitter __ and LinkedIn to read more exclusive content we post.

Zero-Day Alert: Update Chrome Now to Fix New Actively Exploited Vulnerability

Patching every device affected by the LeftoverLocals vulnerability—which includes some iPhones, iPads, and Macs—may prove difficult.

As more companies ramp up development of artificial intelligence systems, they are increasingly turning to graphics processing unit (GPU) chips for the computing power they need to run large language models (LLMs) and to crunch data quickly at massive scale. Between video game processing and AI, demand for GPUs has never been higher, and chipmakers are rushing to bolster supply. In new findings released today, though, researchers are highlighting a vulnerability in multiple brands and models of mainstream GPUs—including Apple, Qualcomm, and AMD chips—that could allow an attacker to steal large quantities of data from a GPU’s memory.

The silicon industry has spent years refining the security of central processing units, or CPUs, so they don’t leak data in memory even when they are built to optimize for speed. However, since GPUs were designed for raw graphics processing power, they haven’t been architected to the same degree with data privacy as a priority. As generative AI and other machine learning applications expand the uses of these chips, though, researchers from New York–based security firm Trail of Bits say that vulnerabilities in GPUs are an increasingly urgent concern.

“There is a broader security concern about these GPUs not being as secure as they should be and leaking a significant amount of data,” Heidy Khlaaf, Trail of Bits’ engineering director for AI and machine learning assurance, tells WIRED. “We’re looking at anywhere from 5 megabytes to 180 megabytes. In the CPU world, even a bit is too much to reveal.”

To exploit the vulnerability, which the researchers call LeftoverLocals, attackers would need to already have established some amount of operating system access on a target’s device. Modern computers and servers are specifically designed to silo data so multiple users can share the same processing resources without being able to access each others’ data. But a LeftoverLocals attack breaks down these walls. Exploiting the vulnerability would allow a hacker to exfiltrate data they shouldn’t be able to access from the local memory of vulnerable GPUs, exposing whatever data happens to be there for the taking, which could include queries and responses generated by LLMs as well as the weights driving the response.

In their proof of concept, as seen in the GIF below, the researchers demonstrate an attack where a target—shown on the left—asks the open source LLM Llama.cpp to provide details about WIRED magazine. Within seconds, the attacker’s device—shown on the right—collects the majority of the response provided by the LLM by carrying out a LeftoverLocals attack on vulnerable GPU memory. The attack program the researchers created uses less than 10 lines of code.

An attacker (right) exploits the LeftoverLocals vulnerability to listen to LLM conversationsVideo: Trail of Bits

Last summer, the researchers tested 11 chips from seven GPU makers and multiple corresponding programming frameworks. They found the LeftoverLocals vulnerability in GPUs from Apple, AMD, and Qualcomm, and launched a far-reaching coordinated disclosure of the vulnerability in September in collaboration with the US-CERT Coordination Center and the Khronos Group, a standards body focused on 3D graphics, machine learning, and virtual and augmented reality.

The researchers did not find evidence that Nvidia, Intel, or Arm GPUs contain the LeftoverLocals vulnerability, but Apple, Qualcomm, and AMD all confirmed to WIRED that they are impacted. This means that well-known chips like the AMD Radeon RX 7900 XT and devices like Apple’s iPhone 12 Pro and M2 MacBook Air are vulnerable. The researchers did not find the flaw in the Imagination GPUs they tested, but others may be vulnerable.

An Apple spokesperson acknowledged LeftoverLocals and noted that the company shipped fixes with its latest M3 and A17 processors, which it unveiled at the end of 2023. This means that the vulnerability is seemingly still present in millions of existing iPhones, iPads, and MacBooks that depend on previous generations of Apple silicon. On January 10, the Trail of Bits researchers retested the vulnerability on a number of Apple devices. They found that Apple’s M2 MacBook Air was still vulnerable, but the iPad Air 3rd generation A12 appeared to have been patched.

A Flaw in Millions of Apple, AMD, and Qualcomm GPUs Could Expose AI Data

This week on the Lock and Code podcast, we tell the true story of a virtual kidnapping scam from December of last year.

_This week on the Lock and Code podcast…_

On Thursday, December 28, at 8:30 pm in the Utah town of Riverdale, the city police began investigating what they believed was a kidnapping.

17-year-old foreign exchange student Kai Zhuang was missing, and according to Riverdale Police Chief Casey Warren, Zhuang was believed to be “forcefully taken” from his home, and “being held against his will.”

The evidence leaned in police’s favor. That night, Zhuang’s parents in China reportedly received a photo of Zhuang in distress. They’d also received a ransom demand.

But as police in Riverdale and across the state of Utah would soon learn, the alleged kidnapping had a few wrinkles.

For starters, there was no sign that Zhuang had been forcefully removed from his home in Riverdale, where he’d been living with his host family. In fact, Zhuang’s disappearance was so quiet that his host family was entirely unaware that he’d been missing until police came and questioned them. Additionally, investigators learned that Zhuang had experienced a recent run-in with police officers nearly 75 miles away in the city of Provo. Just eight days before his disappearance in Riverdale, Zhuang caught the attention of Provo residents because of what they deemed strange behavior for a teenager: Buying camping gear in the middle of a freezing winter season. Police officers who intervened at the residents’ requests asked Zhuang if he was okay, he assured them he was, and a ride was arranged for the teenager back home.

But what Zhuang didn’t tell Provo police at the time was that, already, he was being targeted in an extortion scam. But when Zhuang started to push back against his scammers, it was his parents who became the next target.

Zhuang—and his family—had become victims of what is known as “virtual kidnapping.”

For years, virtual kidnapping scams happened most frequently in Mexico and the Southwestern United States, in cities like Los Angeles and Houston. But in 2015, the scams began reaching farther into the US.

The scams themselves are simple yet cruel attempts at extortion. Virtual kidnappers will call phone numbers belonging to affluent neighborhoods in the US and make bogus threats about a holding a family member hostage.

As explained by the FBI in 2017, virtual kidnappers do not often know the person they are calling, their name, their occupation, or even the name of the family member they have pretended to abduct:

“When an unsuspecting person answered the phone, they would hear a female screaming, ‘Help me!’ The screamer’s voice was likely a recording. Instinctively, the victim might blurt out his or her child’s name: ‘Mary, are you okay?’ And then a man’s voice would say something like, ‘We have Mary. She’s in a truck. We are holding her hostage. You need to pay a ransom and you need to do it now or we are going to cut off her fingers.’”

Today, on the Lock and Code podcast with host David Ruiz, we are presenting a short, true story from December about virtual kidnapping. Today’s episode cites reporting and public statements from the Associated Press, the FBI, ABC4.com, Fox 6 Milwaukee, and the Riverdale Police Department.

Tune in today to listen to the full story.

_Show notes and credits:_

Intro Music: “Spellbound” by Kevin MacLeod (incompetech.com)  
Licensed under Creative Commons: By Attribution 4.0 License  
http://creativecommons.org/licenses/by/4.0/  
Outro Music: “Good God” by Wowa (unminus.com)

**We don’t just report on threats—we remove them**

Cybersecurity risks should never spread beyond a headline. Keep threats off your devices by downloading Malwarebytes today.

 A true tale of virtual kidnapping: Lock and Code S05E02 

Gentoo Linux Security Advisory 202401-24 - Multiple denial of service vulnerabilities have been discovered in Nettle. Versions greater than or equal to 3.9.1 are affected.

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -  
Gentoo Linux Security Advisory                           GLSA 202401-24  
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -  
                                           https://security.gentoo.org/  
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

 Severity: Normal  
    Title: Nettle: Denial of Service  
     Date: January 16, 2024  
     Bugs: #806839, #907673  
       ID: 202401-24

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Synopsis  
=======  
Multiple denial of service vulnerabilities have been discovered in  
Nettle.

Background  
=========  
Nettle is a cryptographic library that is designed to fit easily in  
almost any context: In cryptographic toolkits for object-oriented  
languages, such as C++, Python, or Pike, in applications like lsh or  
GnuPG, or even in kernel space.

Affected packages  
================  
Package          Vulnerable    Unaffected  
---------------  ------------  ------------  
dev-libs/nettle  < 3.9.1       >= 3.9.1

Description  
==========  
Multiple vulnerabilities have been discovered in Nettle. Please review  
the CVE identifiers referenced below for details.

Impact  
=====  
A flaw was found in the way nettle's RSA decryption functions handled  
specially crafted ciphertext. An attacker could use this flaw to provide  
a manipulated ciphertext leading to application crash and denial of  
service.

Workaround  
=========  
There is no known workaround at this time.

Resolution  
=========  
All Nettle users should upgrade to the latest version:

  # emerge --sync  
  # emerge --ask --oneshot --verbose ">Þv-libs/nettle-3.9.1"

References  
=========  
[ 1 ] CVE-2021-3580  
      https://nvd.nist.gov/vuln/detail/CVE-2021-3580  
[ 2 ] CVE-2023-36660  
      https://nvd.nist.gov/vuln/detail/CVE-2023-36660

Availability  
===========  
This GLSA and any updates to it are available for viewing at  
the Gentoo Security Website:

 https://security.gentoo.org/glsa/202401-24

Concerns?  
========  
Security is a primary focus of Gentoo Linux and ensuring the  
confidentiality and security of our users' machines is of utmost  
importance to us. Any security concerns should be addressed to  
security@gentoo.org or alternatively, you may file a bug at  
https://bugs.gentoo.org.

License  
======  
Copyright 2024 Gentoo Foundation, Inc; referenced text  
belongs to its owner(s).

The contents of this document are licensed under the  
Creative Commons - Attribution / Share Alike license.

https://creativecommons.org/licenses/by-sa/2.5

Gentoo Linux Security Advisory 202401-24

Gentoo Linux Security Advisory 202401-23 - A buffer overread vulnerability has been found in libuv. Versions greater than or equal to 1.41.1 are affected.

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -  
Gentoo Linux Security Advisory                           GLSA 202401-23  
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -  
                                           https://security.gentoo.org/  
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

 Severity: Low  
    Title: libuv: Buffer Overread  
     Date: January 16, 2024  
     Bugs: #800986  
       ID: 202401-23

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Synopsis  
=======  
A buffer overread vulnerability has been found in libuv.

Background  
=========  
libuv is a multi-platform support library with a focus on asynchronous  
I/O.

Affected packages  
================  
Package         Vulnerable    Unaffected  
--------------  ------------  ------------  
dev-libs/libuv  < 1.41.1      >= 1.41.1

Description  
==========  
libuv fails to ensure that a pointer lies within the bounds of a defined  
buffer in the uv__idna_toascii() function before reading and  
manipulating the memory at that address.

Impact  
=====  
The overread can result in information disclosure or application crash.

Workaround  
=========  
There is no known workaround at this time.

Resolution  
=========  
All libuv users should upgrade to the latest version:

  # emerge --sync  
  # emerge --ask --oneshot --verbose ">Þv-libs/libuv-1.41.1"

References  
=========  
[ 1 ] CVE-2021-22918  
      https://nvd.nist.gov/vuln/detail/CVE-2021-22918

Availability  
===========  
This GLSA and any updates to it are available for viewing at  
the Gentoo Security Website:

 https://security.gentoo.org/glsa/202401-23

Concerns?  
========  
Security is a primary focus of Gentoo Linux and ensuring the  
confidentiality and security of our users' machines is of utmost  
importance to us. Any security concerns should be addressed to  
security@gentoo.org or alternatively, you may file a bug at  
https://bugs.gentoo.org.

License  
======  
Copyright 2024 Gentoo Foundation, Inc; referenced text  
belongs to its owner(s).

The contents of this document are licensed under the  
Creative Commons - Attribution / Share Alike license.

https://creativecommons.org/licenses/by-sa/2.5

Gentoo Linux Security Advisory 202401-23

Gentoo Linux Security Advisory 202401-22 - Multiple vulnerabilities have been discovered in libspf2, the worst of which can lead to remote code execution. Versions greater than or equal to 1.2.11 are affected.

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -  
Gentoo Linux Security Advisory                           GLSA 202401-22  
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -  
                                           https://security.gentoo.org/  
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

 Severity: Normal  
    Title: libspf2: Multiple vulnerabilities  
     Date: January 15, 2024  
     Bugs: #807739  
       ID: 202401-22

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Synopsis  
=======  
Multiple vulnerabilities have been discovered in libspf2, the worst of  
which can lead to remote code execution.

Background  
=========  
libspf2 is a library that implements the Sender Policy Framework,  
allowing mail transfer agents to make sure that an email is authorized  
by the domain name that it is coming from.

Affected packages  
================  
Package              Vulnerable    Unaffected  
-------------------  ------------  ------------  
mail-filter/libspf2  < 1.2.11      >= 1.2.11

Description  
==========  
Multiple vulnerabilities have been discovered in libspf2. Please review  
the CVE identifiers referenced below for details.

Impact  
=====  
Various buffer overflows have been identified that can lead to denial of  
service and possibly arbitrary code execution.

Workaround  
=========  
There is no known workaround at this time.

Resolution  
=========  
All libspf2 users should upgrade to the latest version:

  # emerge --sync  
  # emerge --ask --oneshot --verbose ">=mail-filter/libspf2-1.2.11"

References  
=========  
[ 1 ] CVE-2021-20314  
      https://nvd.nist.gov/vuln/detail/CVE-2021-20314  
[ 2 ] CVE-2021-33912  
      https://nvd.nist.gov/vuln/detail/CVE-2021-33912  
[ 3 ] CVE-2021-33913  
      https://nvd.nist.gov/vuln/detail/CVE-2021-33913

Availability  
===========  
This GLSA and any updates to it are available for viewing at  
the Gentoo Security Website:

 https://security.gentoo.org/glsa/202401-22

Concerns?  
========  
Security is a primary focus of Gentoo Linux and ensuring the  
confidentiality and security of our users' machines is of utmost  
importance to us. Any security concerns should be addressed to  
security@gentoo.org or alternatively, you may file a bug at  
https://bugs.gentoo.org.

License  
======  
Copyright 2024 Gentoo Foundation, Inc; referenced text  
belongs to its owner(s).

The contents of this document are licensed under the  
Creative Commons - Attribution / Share Alike license.

https://creativecommons.org/licenses/by-sa/2.5

Tag

#mac